Supporting process and quality engineers by automatic diagnosis of cause-and-effect relationships between process variables and quality deficiencies using Data Mining technologies


The through-process detection of cause-and-effect relationships by investigation of process and quality data with Data Mining techniques has been proofed to be a powerful possibility to decrease quality deficiencies. Nevertheless this method was not used area-wide in the companies because of its complexity, the necessary specific knowledge which only few people in the company have and the missing adaptation of the tools to specific problems of the steel production.

These were the reasons for this RFCS project to develop, implement and test robust, practicable and easy-to-use solutions which are specialized to steel quality problems and which overcomes the above deficiencies.

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